Quantum correlation enhanced bound of the information exclusion principle

We investigate the information exclusion principle for multiple measurements with assistance of multiple quantum memories that are well bounded by the upper and lower bounds.The lower bound depends on the observables'complementarity and the complementarity of uncertainty whilst the upper bound includes the complementarity of the observables,quantum discord,and quantum condition entropy.In quantum measurement processing,there exists a relationship between the complementarity of uncertainty and the complementarity of information.In addition,based on the information exclusion principle the complementarity of uncertainty and the shareability of quantum discord can exist as an essential factor to enhance the bounds of each other in the presence of quantum memory.

Applying the maximum information principle to cell transmission model of traffic flow

This paper integrates the maximum information principle with the Cell Transmission Model （CTM） to formulate the velocity distribution evolution of vehicle traffic flow. The proposed discrete traffic kinetic model uses the cell transmission model to calculate the macroscopic variables of the vehicle transmission, and the maximum information principle to examine the velocity distribution in each cell. The velocity distribution based on maximum information principle is solved by the Lagrange multiplier method. The advantage of the proposed model is that it can simultaneously calculate the hydrodynamic variables and velocity distribution at the cell level. An example shows how the proposed model works. The proposed model is a hybrid traffic simulation model, which can be used to understand the self-organization phenomena in traffic flows and predict the traffic evolution.

Empirical Evidence for a Descriptive Model of Principles of Information Security Course

The purpose of this study is to examine the nature and content of the rapidly evolving undergraduate Principles of Information/Cybersecurity course which has been attracting an ever-growing attention in the computing discipline, for the past decade. More specifically, it is to provide an impetus for the design of standardized principles of Information/Cybersecurity course. To achieve this, a survey of colleges and universities that offer the course was conducted. Several schools of engineering and business, in universities and colleges across several countries were surveyed to generate necessary data. Effort was made to direct the questionnaire only to Computer Information System (CIS), Computer Science (CS), Management Information System (MIS), Information System (IS) and other computer-related departments. The study instrument consisted of two main parts: one part addressed the institutional demographic information, while the other focused on the relevant elements of the course. There are sixty-two (62) questionnaire items covering areas such as demographics, perception of the course, course content and coverage, teaching preferences, method of delivery and course technology deployed, assigned textbooks and associated resources, learner support, course assessments, as well as the licensure-based certifications. Several themes emerged from the data analysis: (a) the principles course is an integral part of most cybersecurity programs;(b) majority of the courses examined, stress both strong technical and hands-on skills;(c) encourage vendor-neutral certifications as a course exit characteristic;and (d) an end-of-course class project, remains a standard requirement for successful course completion. Overall, the study makes it clear that cybersecurity is a multilateral discipline, and refuses to be confined by context and content. It is envisaged that the results of this study would turn out to be instructive for all practical purposes. We expect it to be one of the most definitive descriptive models of such a cardinal course, and help to guide and actually, shape the decisions of universities and academic programs focusing on information/cyber security in the updating and upgrading their curricula, most especially, the foundational principles course in light of new findings that are herein articulated.

Granular Computing for Data Analytics:A Manifesto of Human-Centric Computing

In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data.We advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular Computing.Granular Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there.This study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.

Formation Principles of Similar Systems

In this paper some new concepts, i.e, Ordered Structure of Systems, Universal Information, Strength of Information Field are proposed. Based on Similarity System Theory and Similology, five principles for investigating similarity formation, i.e., Principle of Ordered Structue, Information Principle, Consanguining Principle, Coadaptation Principle and Governing Principle are introduced. The origin of similar systems, and way of similarity formation are expounded. Thus, formation principles of similar systems is presented.

Multivariate small area estimation under nonignorable nonresponse

We consider multivariate small area estimation under nonignorable, not missing at random(NMAR) nonresponse. We assume a response model that accounts for the different patterns ofthe observed outcomes, (which values are observed and which ones are missing), and estimatethe response probabilities by application of the Missing Information Principle (MIP). By this principle, we first derive the likelihood score equations for the case where the missing outcomes areactually observed, and then integrate out the unobserved outcomes from the score equationswith respect to the distribution holding for the missing data. The latter distribution is definedby the distribution fitted to the observed data for the respondents and the response model. Theintegrated score equations are then solved with respect to the unknown parameters indexingthe response model. Once the response probabilities have been estimated, we impute the missing outcomes from their appropriate distribution, yielding a complete data set with no missingvalues, which is used for predicting the target area means. A parametric bootstrap procedure isdeveloped for assessing the mean squared errors (MSE) of the resulting predictors. We illustratethe approach by a small simulation study.